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	ऑब्जेक्ट डिटेक्शन के साथ ट्रांसफर लर्निंग	फाइन-ट्यून्ड कन्волюशनल न्यूरल नेटवर्क
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2010–2014	2012–2014
प्रवर्तक	Girshick, R. et al. (R-CNN line); Pan & Yang (transfer learning framework)	Yosinski, J. et al. (theoretical basis); practice widespread from Krizhevsky et al. 2012 onward
प्रकार	Transfer learning / fine-tuning	Transfer learning technique (supervised fine-tuning)
मौलिक स्रोत	Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27.
उपनाम	pretrained object detector, fine-tuned object detection, TL-OD, domain-adapted object detection	Fine-tuned CNN, CNN fine-tuning, CNN transfer learning with fine-tuning, adapted convolutional network
संबंधित	3	5
सारांश	Transfer learning with object detection starts from a deep neural network pretrained on a large image dataset — typically ImageNet for the backbone or COCO for the full detector — and adapts it to detect objects in a new domain. By reusing learned visual representations, it achieves strong detection accuracy with far fewer annotated images than training from scratch would require.	Fine-tuning a CNN means starting from a network already trained on a large dataset — typically ImageNet — and continuing training on a smaller target dataset so the model adapts its learned visual features to a new task. This approach dramatically reduces the data and compute required to reach strong performance compared with training from scratch.